Model Integration and Configuration for Credit Application

What the Data for This Use Case Looks Like

Credit application risk control data primarily comes from paper or electronic materials submitted by applicants. It also includes authorized or public data such as credit reports, industrial and commercial records, tax records, and operating bank flows synced by partner institutions. Static materials like business licenses and income proofs are one-time submitted unstructured documents. Dynamic data such as tax amounts and bank flows updates monthly. Documents include structured fields: applicant identification numbers, requested credit limits, and submission times. They also include unstructured scanned files and PDF flow reports. Some fields have clear units: total tax is measured in ten thousand yuan, and flow cycles use natural months.

Constraints Imposed by These Data Characteristics on Model Integration and Configuration

Multi-source heterogeneous data for credit applications requires the model integration link to support mixed configuration for structured field extraction and unstructured document parsing. Static one-time submitted materials may include multi-page long documents. Parameter rules for long text segmentation and context stitching must be supported. Dynamically updated tax and flow data requires scheduled synchronization trigger configuration parameters. This avoids using expired data. Materials contain sensitive information such as identity and financial details. Pre-configured data desensitization items must be bound to prevent sensitive field leaks. Additionally, material formats vary widely across different applicants. General file parsing adaptation rules must be configured.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`300–600 seconds`	Credit application flow PDFs may contain multi-page long text, so sufficient parsing time must be reserved
`maxContext`	`8000–12000 characters`	Credit application materials often contain long contexts formed by multiple spliced documents, so long context windows must be supported
`Recall Count`	`Top 8–12 entries`	Credit risk control requires combining multi-dimensional related data such as credit reports, bank flows, and tax records. Sufficient relevant knowledge base entries must be recalled
`Similarity Threshold`	`0.75–0.85`	Balance precision and recall rates, avoid missing weakly correlated but critical risk control data
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Single files such as credit application flow reports and credit reports may have large sizes
`ENABLE_SENSITIVE_MASK`	`Enabled`	Credit application materials contain sensitive information such as identity and financial details. Enable desensitization configuration to ensure data compliance

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Misconfigurations

Issue: Parsed credit application material fields are missing or incorrect, and a PARSE_FAILED status code is returned. Cause: No reasonable PARSE_FILE_TIMEOUT_SECONDS parameter is set. Long documents such as monthly flow reports time out before parsing completes.
Issue: When deploying a model locally, audit results for the same credit application vary randomly each time. Results are consistent when deployed online. Cause: The LLM_TEMPERATURE parameter is not fixed. Local models use a higher default temperature value, leading to strong output randomness.
Issue: Uploaded files such as business licenses and flow reports fail to submit, and an upload failure prompt appears in the interface. Cause: The UPLOAD_FILE_MAX_SIZE parameter is not adjusted. Single file size exceeds the platform's default limit.

How to Verify Successful Configuration

Upload a typical credit application material. Check if the parsed structured fields fully match the material content. Verify that sensitive fields have been desensitized.
Submit three identical credit audit requests. Confirm results are consistent for online deployments. For local deployments, confirm results have no random differences after fixing the LLM_TEMPERATURE parameter.
Test credit application materials of different sizes. Confirm that upload and parsing processes complete normally, with no timeouts or errors.
Adjust the Recall Count and Similarity Threshold parameters. Verify that the number and relevance of recalled knowledge base entries meet credit risk control audit requirements.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.